# EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication

arXiv: https://arxiv.org/abs/2102.08026 · HTML: https://mlsanity.com/paper/2102.08026/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 69 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network](https://mlsanity.com/paper/2101.01666/) (2020)
- [A database for face presentation attack using wax figure faces](https://mlsanity.com/paper/1906.11900/) (2019)

## Built on by (most cited first, 7 of 7)

- [COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network](https://mlsanity.com/paper/2106.00436/) (2021)
- [A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance](https://mlsanity.com/paper/2207.07089/) (2022)
- [RamanNet: A generalized neural network architecture for Raman Spectrum Analysis](https://mlsanity.com/paper/2201.09737/) (2022)
- [Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification](https://mlsanity.com/paper/2204.07246/) (2022)
- [Modally Reduced Representation Learning of Multi-Lead ECG Signals through Simultaneous Alignment and Reconstruction](https://mlsanity.com/paper/2405.19359/) (2024)
- [Hear the Heartbeat in Phases: Physiologically Grounded Phase-Aware ECG Biometrics](https://mlsanity.com/paper/2601.00170/) (2026)
- [Learning Cardiac Features: ECG Biometrics Across Time and~Exercise](https://mlsanity.com/paper/2609.21962/) (2026)

## Abstract (arXiv)

In recent years, physiological signal based authentication has shown great promises,for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using less number of beats. EDITH performs competitively using just a single heartbeat (96-99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29%. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.